Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.739
A stress detection based on multi-class probabilistic support vector machines (MCP-SVMs) is proposed for classifying speech into following categories - no stress, primary stress, and secondary stress. The stress classifier is performed with a feature set including perceptual features, MFCC, delta-MFCC and delta-delta-MFCC. To observe that speakers from the same accent regions had similar tendencies in mispronunciations including word stress, this work uses English Across Taiwan (EAT) to represent Taiwanese-accented English speech corpora. The overall performance in the experimental results achieves about 84% classification of accuracy.
stress detection, English Across Taiwan, multi-class probabilistic support vector machines
Jhing-Fa Wang, Gung-Ming Chang, Jia-Ching Wang, Shun-Chieh Lin, "Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech", CSIE, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 346-350, doi:10.1109/CSIE.2009.739